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Last Friday I defended my thesis about #Conservation #genomics in Scandinavian #Wolves at @UU_University. I enjoyed every second of the day, especially the discussion with my opponent Giorgio Bertorelle. A great end to an era in my life. Thanks to everyone who's been part of it♥️

Link to paper:

In conclusion we have shown that it is possible to study SVs in a natural population using only short read data, and that they can be used to assess genetic load and population structure just as SNPs. (8/8).

We were also interested in how load was affected by a natural genetic rescue event, and compared descendants to the original population with descendants to immigrants from the same period. The realized load decreased significantly with immigration. (7/8)

Finally we assessed the masked and realized load, approximated by the genotype frequency of deleterious variants in heterozygous and homozygous states, respectively, and saw that the masked load increased with inbreeding, as was previously shown with SNPs. (6/8)

We polarized all variants using two outgroups and compared the SFS for SVs in coding sequence (=putatively deleterious) to those in introns, and saw that the spectrum for SVs that interrupt CDS is shifted to the left, indicative of purifying selection. (5/8)

When using genotypes of SVs to assess population structure, we largely retrieved the same pattern as for SNPs, despite the much smaller number of markers (25,640 deletions, 786 duplications and 126 inversions, compared to >8 million SNPs). (4/8)

There was an excess of deletions and duplications of lengths around 150-200 bp, they turned out to largely overlap with transposable elements (SINE for deletions and LINE for duplications). (3/8)

We identified and called SVs only from short read data. There were naturally many false positives, but through manual visual inspection with Samplot and PlotCritic we narrowed it down to a high confidence set of ~26,000 variants (mostly deletions). (2/8)

Our paper about structural variants in inbred Scandinavian #wolves just came out in @EvolAppJournal! See 🧵 below on how we used them to investigate genetic load⬇ (1/8).

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